Neural networks (NNs) fail to perform well or make excessive predictions when predicting out-of-distribution or unseen datasets. In contrast, Bayesian neural networks (BNNs) can quantify the uncertainty of their inference to solve this problem. Nevertheless, BNNs have not been widely adopted owing to their increased memory and computational cost. In this study, we propose a novel approach to extend binary neural networks by introducing a probabilistic interpretation of binary weights, effectively converting them into BNNs. The proposed approach can reduce the number of weights by half compared to the conventional method. A comprehensive comparative analysis with established methods like Monte Carlo dropout and Bayes by backprop was performed to assess the performance and capabilities of our proposed technique in terms of accuracy and capturing uncertainty. Through this analysis, we aim to provide insights into the advantages of this Bayesian extension.

With the advent of gated quantum computers and the regular structures for qubit layout, methods for placement, routing, noise estimation, and logic to hardware mapping become imminently required. In this paper, we propose a method for quantum circuit layout that is intended to solve such problems when mapping a quantum circuit to a gated quantum computer. The proposed methodology starts by building a Circuit Interaction Graph (CIG) that represents the ideal hardware layout minimizing the distance and path length between the individual qubits. The CIG is also used to introduce a qubit noise model. Once constructed, the CIG is iteratively reduced to a given architecture (qubit coupling model) specifying the neighborhood, qubits, priority, and qubits noise. The introduced constraints allow us to additionally reduce the graph according to preferred weights of desired properties. We propose two different methods of reducing the CIG: iterative reduction or the iterative isomorphism search algorithm. The proposed method is verified and tested on a set of standard benchmarks with results showing improvement on certain functions while in average improving the cost of the implementation over the current state of the art methods.

This paper proposes a decomposition method for symmetric multiple-valued functions. It decomposes a given symmetric multiple-valued function into three parts. By using suitable decision diagrams for the three parts, we can represent symmetric multiple-valued functions compactly. By deriving theorems on sizes of the decision diagrams, this paper shows that space complexity of the proposed representation is low. This paper also presents algorithms to construct the decision diagrams for symmetric multiple-valued functions with low time complexity. Experimental results show that the proposed method represents randomly generated symmetric multiple-valued functions more compactly than the conventional representation method using standard multiple-valued decision diagrams. Symmetric multiple-valued functions are a basic class of functions, and thus, their compact representation benefits many applications where they appear.

This paper shows that sum-of-product expression (SOP) minimization produces the generalization ability. We show this in three steps. First, various classes of SOPs are generated. Second, minterms of SOP are randomly selected to generate partially defined functions. And, third, from the partially defined functions, original functions are reconstructed by SOP minimization. We consider Achilles heel functions, majority functions, monotone increasing cascade functions, functions generated from random SOPs, monotone increasing random SOPs, circle functions, and globe functions. As for the generalization ability, the presented method is compared with Naive Bayes, multi-level perceptron, support vector machine, JRIP, J48, and random forest. For these functions, in many cases, only 10% of the input combinations are sufficient to reconstruct more than 90% of the truth tables of the original functions.

In this paper, we consider an orthogonal frequency division multiple access (OFDMA)-based multiuser full-duplex wireless powered communication network (FD WPCN) system with beamforming (BF) at an energy transmitter (ET). The ET performs BF to efficiently transmit energy to multiple users while suppressing interference to an information receiver (IR). Multiple users operating in full-duplex mode harvest energy from the signals sent by the ET while simultaneously transmitting information to the IR using the harvested energy. We analytically demonstrate that the FD WPCN is superior to its half-duplex (HD) WPCN counterpart in the high-SNR regime. We propose a transmitter design method that maximizes the sum rate by determining the BF at the ET, power allocation at both the ET and users, and sub-band allocation. Simulation results show the effectiveness of the proposed method.

This paper proposes a novel differential active self-interference canceller (DASIC) algorithm for asynchronous in-band full-duplex (IBFD) Gaussian filtered frequency shift keying (GFSK), which is designed for wireless Internet of Things (IoT). In IBFD communications, where two terminals simultaneously transmit and receive signals in the same frequency band, there is an extremely strong self-interference (SI). The SI can be mitigated by an active SI canceller (ASIC), which subtracts an interference replica based on channel state information (CSI) from the received signal. The challenging problem is the realization of asynchronous IBFD for wireless IoT in indoor environments. In the asynchronous mode, pilot contamination is induced by the non-orthogonality between asynchronous pilot sequences. In addition, the transceiver suffers from analog front-end (AFE) impairments, such as phase noise. Due to these impairments, the SI cannot be canceled entirely at the receiver, resulting in residual interference. To address the above issue, the DASIC incorporates the principle of the differential codec, which enables to suppress SI without the CSI estimation of SI owing to the differential structure. Also, on the premise of using an error correction technique, iterative detection and decoding (IDD) is applied to improve the detection capability while exchanging the extrinsic log-likelihood ratio (LLR) between the maximum a-posteriori probability (MAP) detector and the channel decoder. Finally, the validity of using the DASIC algorithm is evaluated by computer simulations in terms of the packet error rate (PER). The results clearly demonstrate the possibility of realizing asynchronous IBFD.

This study presents a novel waveguide slot array with a code-division multiplexing function for single RF chain digital beamforming. The proposed antenna is comprised of a rectangular metallic waveguide’s bottom part and a multilayer printed circuit board (PCB) with the rectangular waveguide’s top wall and slot apertures. Multiple pairs of two symmetric longitudinal slots are etched on the metal surface of the PCB, and a PIN diode is mounted across each slot. The received signals of each slot pair are multiplexed in a code-division multiplexing fashion by switching the diodes’ bias according to the Walsh Hadamard code, and the original signals are then recovered through a despreading process in the digital domain for digital beamforming. A prototype antenna with eight slot pairs has been fabricated and tested for proof of concept. The measured results show the feasibility of the proposed antenna.

In face super-resolution reconstruction, the interference caused by the texture and color of the hair region on the details and contours of the face region can negatively affect the reconstruction results. This paper proposes a semantic-based, dual-branch face super-resolution algorithm to address the issue of varying reconstruction complexities and mutual interference among different pixel semantics in face images. The algorithm clusters pixel semantic data to create a hierarchical representation, distinguishing between facial pixel regions and hair pixel regions. Subsequently, independent image enhancement is applied to these distinct pixel regions to mitigate their interference, resulting in a vivid, super-resolution face image.

In this paper, we propose a method for video reflection removal using a video restoration framework with enhanced deformable networks (EDVR). We examine the effect of each module in EDVR on video reflection removal and modify the models using 3D convolutions. The performance of each modified model is evaluated in terms of the RMSE between the structural similarity (SSIM) and the smoothed SSIM representing temporal consistency.

In this letter, we study the dynamic antenna grouping and the hybrid beamforming for high altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. We first exploit the fact that the ergodic sum rate is only related to statistical channel state information (SCSI) in the large-scale array regime, and then we utilize it to perform the dynamic antenna grouping and design the RF beamformer. By applying the Gershgorin Circle Theorem, the dynamic antenna grouping is realized based on the novel statistical distance metric instead of the value of the instantaneous channels. The RF beamformer is designed according to the singular value decomposition of the statistical correlation matrix according to the obtained dynamic antenna group. Dynamic subarrays mean each RF chain is linked with a dynamic antenna sub-set. The baseband beamformer is derived by utilizing the zero forcing (ZF). Numerical results demonstrate the performance enhancement of our proposed dynamic hybrid precoding (DHP) algorithm.

In underwater acoustic communication systems based on orthogonal frequency division multiplexing (OFDM), taking clipping to reduce the peak-to-average power ratio leads to nonlinear distortion of the signal, making the receiver unable to recover the faded signal accurately. In this letter, an Aquila optimizer-based convolutional attention block stacked network (AO-CABNet) is proposed to replace the receiver to improve the ability to recover the original signal. Simulation results show that the AO method has better optimization capability to quickly obtain the optimal parameters of the network model, and the proposed AO-CABNet structure outperforms existing schemes.

Point cloud video contains not only color information but also spatial position information and usually has large volume of data. Typical rate distortion optimization algorithms based on Human Visual System only consider the color information, which limit the coding performance. In this paper, a Coding Tree Unit (CTU) level quantization parameter (QP) adjustment algorithm based on JND and spatial complexity is proposed to improve the subjective and objective quality of Video-Based Point Cloud Compression (V-PCC). Firstly, it is found that the JND model is degraded at CTU level for attribute video due to the pixel filling strategy of V-PCC, and an improved JND model is designed using the occupancy map. Secondly, a spatial complexity detection metric is designed to measure the visual importance of each CTU. Finally, a CTU-level QP adjustment scheme based on both JND levels and visual importance is proposed for geometry and attribute video. The experimental results show that, compared with the latest V-PCC (TMC2-18.0) anchors, the BD-rate is reduced by -2.8% and -3.2% for D1 and D2 metrics, respectively, and the subjective quality is improved significantly.

Mutually unbiased bases (MUBs) are widely used in quantum information processing and play an important role in quantum cryptography, quantum state tomography and communications. It’s difficult to construct MUBs and remains unknown whether complete MUBs exist for any non prime power. Therefore, researchers have proposed the solution to construct approximately mutually unbiased bases (AMUBs) by weakening the inner product conditions. This paper constructs q AMUBs of ℂq, (q + 1) AMUBs of ℂq-1 and q AMUBs of ℂq-1 by using character sums over Galois rings and finite fields, where q is a power of a prime. The first construction of q AMUBs of ℂq is new which illustrates K AMUBs of ℂK can be achieved. The second and third constructions in this paper include the partial results about AMUBs constructed by W. Wang et al. in [9].

Boolean functions play an important role in symmetric ciphers. One of important open problems on Boolean functions is determining the maximum possible resiliency order of n-variable Boolean functions with optimal algebraic immunity. In this letter, we search Boolean functions in the rotation symmetric class, and determine the maximum possible resiliency order of 9-variable Boolean functions with optimal algebraic immunity. Moreover, the maximum possible nonlinearity of 9-variable rotation symmetric Boolean functions with optimal algebraic immunity-resiliency trade-off is determined to be 224.

To improve the control performance of the permanent magnet synchronous motor speed control system, the fractional order calculus theory is combined with the sliding mode control to design the fractional order integral sliding mode sliding mode surface (FOISM) to improve the robustness of the system. Secondly, considering the existence of chattering phenomenon in sliding mode control, a new second-order sliding mode reaching law (NSOSMRL) is designed to improve the control accuracy of the system. Finally, the effectiveness of the proposed strategy is demonstrated by simulation.

In this letter, a novel projection algorithm is proposed in which projection onto a triangle consisting of the three even-vertices closest to the vector to be projected replaces check polytope projection, achieving the same FER performance as exact projection algorithm in both high-iteration and low-iteration regime. Simulation results show that compared with the sparse affine projection algorithm (SAPA), it can improve the FER performance by 0.2 dB as well as save average number of iterations by 4.3%.

High Frequency Surface Wave Radar holds significant potential in sea detection. However, the target signals are often surpassed by substantial sea clutter and ionospheric clutter, making it crucial to address clutter suppression and extract weak target signals amidst the strong noise background.This study proposes a novel method for separating weak harmonic target signals based on local tangent space, leveraging the chaotic feature of ionospheric clutter.The effectiveness of this approach is demonstrated through the analysis of measured data, thereby validating its practicality and potential for real-world applications.

To improve the direction-of-arrival estimation performance of the small-aperture array, we propose a source localization method inspired by the Ormia fly’s coupled ears and MUSIC-like algorithm. The Ormia can local its host cricket’s sound precisely despite the tremendous incompatibility between the spacing of its ear and the sound wavelength. In this paper, we first implement a biologically inspired coupled system based on the coupled model of the Ormia’s ears and solve its responses by the modal decomposition method. Then, we analyze the effect of the system on the received signals of the array. Research shows that the system amplifies the amplitude ratio and phase difference between the signals, equivalent to creating a virtual array with a larger aperture. Finally, we apply the MUSIC-like algorithm for DOA estimation to suppress the colored noise caused by the system. Numerical results demonstrate that the proposed method can improve the localization precision and resolution of the array.

Devices presenting audiovisual information are widespread, but few ones presenting olfactory information. We have developed a device called an olfactory display that presents odors to users by mixing multiple fragrances. Previously developed olfactory displays had the problem that the ejection volume of liquid perfume droplets was large and the dynamic range of the blending ratio was small. In this study, we used an inkjet device that ejects small droplets in order to expand the dynamic range of blending ratios to present a variety of scents. By finely controlling the back pressure using an electro-osmotic pump (EO pump) and adjusting the timing of EO pump and inkjet device, we succeeded in stabilizing the ejection of the inkjet device and we can have large dynamic range.

Diagnosis at an early stage is clinically important for the cure of skin cancer. However, since some skin cancers have similar intuitive characteristics, and dermatologists rely on subjective experience to distinguish skin cancer types, the accuracy is often suboptimal. Recently, the introduction of computer methods in the medical field has better assisted physicians to improve the recognition rate but some challenges still exist. In the face of massive dermoscopic image data, residual network (ResNet) is more suitable for learning feature relationships inside big data because of its deeper network depth. Aiming at the deficiency of ResNet, this paper proposes a multi-region feature extraction and raising dimension matching method, which further improves the utilization rate of medical image features. This method firstly extracted rich and diverse features from multiple regions of the feature map, avoiding the deficiency of traditional residual modules repeatedly extracting features in a few fixed regions. Then, the fused features are strengthened by up-dimensioning the branch path information and stacking it with the main path, which solves the problem that the information of two paths is not ideal after fusion due to different dimensionality. The proposed method is experimented on the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains more than 40,000 images. The results of this work on this dataset and other datasets are evaluated to be improved over networks containing traditional residual modules and some popular networks.